Importing libraries and dataset¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as px

%matplotlib inline
In [2]:
import cufflinks as cf
import chart_studio.plotly as py
import plotly.express as px
import plotly.graph_objects as go
import statistics as stat
In [3]:
nd = pd.read_csv("/Users/lakshmiprasanna/Documents/netflix/netflix.csv",encoding='latin-1')
In [4]:
nd
Out[4]:
title rating ratingLevel ratingDescription release year user rating score user rating size
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82.0 80
1 Lucky Number Slevin R strong violence, sexual content and adult lang... 100 2006 NaN 82
2 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98.0 80
3 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98.0 80
4 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94.0 80
... ... ... ... ... ... ... ...
995 The BFG PG for action/peril, some scary moments and brief... 60 2016 97.0 80
996 The Secret Life of Pets PG for action and some rude humor 60 2016 NaN 81
997 Precious Puppies TV-G Suitable for all ages. 35 2003 NaN 82
998 Beary Tales TV-G Suitable for all ages. 35 2013 NaN 82
999 Growing Up Wild G NaN 35 2016 80.0 80

1000 rows × 7 columns

Data Cleaning and obtaining general idea¶

In [5]:
nd['user rating score'].isnull().sum()
Out[5]:
395
In [6]:
nd = nd.dropna(subset='user rating score')
In [7]:
nd = nd.reset_index(drop=True)
In [8]:
nd.columns
Out[8]:
Index(['title', 'rating', 'ratingLevel', 'ratingDescription', 'release year',
       'user rating score', 'user rating size'],
      dtype='object')
In [9]:
nd['rating'].unique()
Out[9]:
array(['PG-13', 'TV-14', 'TV-PG', 'TV-MA', 'NR', 'TV-Y', 'TV-Y7-FV', 'PG',
       'R', 'TV-G', 'G', 'TV-Y7'], dtype=object)
In [10]:
nd['user rating score'].unique()
Out[10]:
array([82., 98., 94., 95., 97., 91., 96., 77., 88., 80., 74., 81., 57.,
       84., 83., 99., 89., 92., 62., 90., 79., 93., 61., 78., 66., 75.,
       63., 68., 71., 59., 73., 86., 69., 58., 55., 56., 64., 85., 70.,
       67., 72., 65.])
In [11]:
nd['user rating score'] = nd['user rating score'].astype('int64')
In [12]:
nd['user rating size'].unique()
Out[12]:
array([80])
As user rating size is the same for all the values, we can consider just user rating score for the analysis¶
In [13]:
nd.head()
Out[13]:
title rating ratingLevel ratingDescription release year user rating score user rating size
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82 80
1 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80
2 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80
3 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94 80
4 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80
Ques: Finding which shows the audience liked¶
In [14]:
array_rateDesc = list(nd['ratingDescription'].unique())
array_rateDesc.sort(reverse=True)
array_rateDesc
Out[14]:
[124, 110, 100, 90, 80, 70, 60, 42, 41, 35, 10]
In [15]:
array_ratings = []
for i in array_rateDesc:
    array_ratings.append(nd.loc[nd['ratingDescription'] == i, 'rating'].values[0])
print(array_ratings)

['NR', 'TV-MA', 'R', 'TV-14', 'PG-13', 'TV-PG', 'PG', 'TV-Y7-FV', 'TV-Y7', 'TV-G', 'TV-Y']
In [16]:
dict = {r:rd for r,rd in zip(array_ratings,array_rateDesc)}
print(dict)

{'NR': 124, 'TV-MA': 110, 'R': 100, 'TV-14': 90, 'PG-13': 80, 'TV-PG': 70, 'PG': 60, 'TV-Y7-FV': 42, 'TV-Y7': 41, 'TV-G': 35, 'TV-Y': 10}
In [17]:
nd
Out[17]:
title rating ratingLevel ratingDescription release year user rating score user rating size
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82 80
1 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80
2 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80
3 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94 80
4 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80
... ... ... ... ... ... ... ...
600 Pok̩mon: Indigo League TV-Y Suitable for all ages. 10 2000 74 80
601 Paddington PG mild action and rude humor 60 2014 70 80
602 Dawn of the Croods TV-Y7 Suitable for children ages 7 and older 41 2017 72 80
603 The BFG PG for action/peril, some scary moments and brief... 60 2016 97 80
604 Growing Up Wild G NaN 35 2016 80 80

605 rows × 7 columns

In [18]:
rate_count = []
rate_sum = []
avg_rate = []
median_rate = []
most_repeated_rate = []

for i in array_ratings:
    rate_count.append(nd[nd['rating']==i]['user rating score'].count())
    rate_sum.append(nd[nd['rating']==i]['user rating score'].sum())
    avg_rate.append(nd[nd['rating']==i]['user rating score'].mean())
    median_rate.append(nd[nd['rating']==i]['user rating score'].median())
    most_repeated_rate.append(nd[nd['rating']==i]['user rating score'].mode())
In [19]:
rate_count
Out[19]:
[4, 96, 9, 188, 11, 42, 119, 16, 14, 24, 25]
In [20]:
diction = {'Rating genre': array_ratings,'Count' : rate_count, 'Overall ratings sum' : rate_sum, 
           'Average rating' : avg_rate, 'Median rating' : median_rate}
ratings_df = pd.DataFrame(diction)
In [21]:
ratings_df
Out[21]:
Rating genre Count Overall ratings sum Average rating Median rating
0 NR 4 348 87.000000 97.0
1 TV-MA 96 8498 88.520833 93.0
2 R 9 784 87.111111 91.0
3 TV-14 188 16248 86.425532 91.0
4 PG-13 11 814 74.000000 77.0
5 TV-PG 42 3606 85.857143 92.0
6 PG 119 10258 86.201681 91.0
7 TV-Y7-FV 16 1205 75.312500 73.0
8 TV-Y7 14 1035 73.928571 72.0
9 TV-G 24 1788 74.500000 74.0
10 TV-Y 25 1890 75.600000 77.0
In [22]:
mode=[]
a = []
for i in range(len(most_repeated_rate)):
    for j in range(len(most_repeated_rate[i])):
        a.append(most_repeated_rate[i][j])
    mode.append(stat.mean(a))
    a=[]
mode
Out[22]:
[97, 96, 88, 98, 82, 94, 97, 80, 72, 74, 81]
In [23]:
ratings_df['Mode'] = mode
ratings_df
Out[23]:
Rating genre Count Overall ratings sum Average rating Median rating Mode
0 NR 4 348 87.000000 97.0 97
1 TV-MA 96 8498 88.520833 93.0 96
2 R 9 784 87.111111 91.0 88
3 TV-14 188 16248 86.425532 91.0 98
4 PG-13 11 814 74.000000 77.0 82
5 TV-PG 42 3606 85.857143 92.0 94
6 PG 119 10258 86.201681 91.0 97
7 TV-Y7-FV 16 1205 75.312500 73.0 80
8 TV-Y7 14 1035 73.928571 72.0 72
9 TV-G 24 1788 74.500000 74.0 74
10 TV-Y 25 1890 75.600000 77.0 81
Ans: Top 3 genres based on count:¶

  1. TV-14 : TV-14 stands for content that may be inappropriate for children younger than 14 years of age.

  2. PG : PG stands for content that should be watched under parental guidance.

  3. TV-MA: TV-MA is a rating assigned by the TV Parental Guidelines to a television program that was designed for mature audiences only.

Ans: Top 3 genres based on rating:¶

(I'm considering median because it gives the best idea of what people think of the genre)

  1. NR : NR rating refers to a movie that has not yet been rated. This could mean that the movie has not been submitted for a rating or is an uncut version of a movie that was submitted.

  2. TV-MA : TV-MA is a rating assigned by the TV Parental Guidelines to a television program that was designed for mature audiences only.

  3. TV-PG : TV-PG is a rating for the TV shows that are to be watched under parental guidance.

In [24]:
ratings_df['Weighted average'] = np.random.rand(len(ratings_df))
In [25]:
for i in range(len(ratings_df)):
    ratings_df['Weighted average'][i] = (ratings_df['Count'][i]+ratings_df['Median rating'][i])/2
ratings_df
Out[25]:
Rating genre Count Overall ratings sum Average rating Median rating Mode Weighted average
0 NR 4 348 87.000000 97.0 97 50.5
1 TV-MA 96 8498 88.520833 93.0 96 94.5
2 R 9 784 87.111111 91.0 88 50.0
3 TV-14 188 16248 86.425532 91.0 98 139.5
4 PG-13 11 814 74.000000 77.0 82 44.0
5 TV-PG 42 3606 85.857143 92.0 94 67.0
6 PG 119 10258 86.201681 91.0 97 105.0
7 TV-Y7-FV 16 1205 75.312500 73.0 80 44.5
8 TV-Y7 14 1035 73.928571 72.0 72 43.0
9 TV-G 24 1788 74.500000 74.0 74 49.0
10 TV-Y 25 1890 75.600000 77.0 81 51.0
In [26]:
go.Figure(data=go.Box(x=nd['rating'],y=nd['user rating score']))
Ans: As both count and median are not in sync, weighted average of them can help us in understanding what consumers like from the shows that are on netflix. They are:¶
  1. TV-14
  2. PG
  3. TV-MA
Ans: Although it's the same order as that of number of shows on netflix, it can also be said that the audience might like these genres better, considering it is a weighted average. That being said, the creators can also go for top rated shows as it's always the storyline that the audience find appealing.¶
Ques: As the rating description values are in decreasing order, does that mean the higher rating type are preferred? or are they just a code numbers? We can decide based on user rating score¶
In [27]:
traces = []
for column in ratings_df.columns[3:]:
    trace = go.Scatter(x=ratings_df[column], y=ratings_df['Rating genre'], mode='lines', name=column)
    traces.append(trace)
    
go.Figure(data=traces)
Ans: As from the graphs, we can say that the rating description values are just random numbers as the order of numbers are random and not in relation with the rating values.¶
Ques: Analysis of Movies vs TV Shows¶
In [28]:
array_ratings
Out[28]:
['NR',
 'TV-MA',
 'R',
 'TV-14',
 'PG-13',
 'TV-PG',
 'PG',
 'TV-Y7-FV',
 'TV-Y7',
 'TV-G',
 'TV-Y']
In [29]:
TV_shows = []
Movies_and_others = []
Type = ('TV shows', 'Movies and others')

for i in range(len(array_ratings)):
    if (array_ratings[i][0] == 'T' and array_ratings[i][1] == 'V'):
        TV_shows.append(array_ratings[i])
    else:
        Movies_and_others.append(array_ratings[i])
In [30]:
TV_shows
Out[30]:
['TV-MA', 'TV-14', 'TV-PG', 'TV-Y7-FV', 'TV-Y7', 'TV-G', 'TV-Y']
In [31]:
Movies_and_others
Out[31]:
['NR', 'R', 'PG-13', 'PG']
In [32]:
nd['type label'] = np.random.rand(len(nd))
In [33]:
for i in range(len(nd)):
    if nd['rating'][i] in TV_shows:
        nd['type label'][i] = "TV shows"
    else:
        nd['type label'][i] = "Movies and others"
In [34]:
nd
Out[34]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82 80 Movies and others
1 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80 TV shows
2 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80 TV shows
3 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94 80 TV shows
4 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80 TV shows
... ... ... ... ... ... ... ... ...
600 Pok̩mon: Indigo League TV-Y Suitable for all ages. 10 2000 74 80 TV shows
601 Paddington PG mild action and rude humor 60 2014 70 80 Movies and others
602 Dawn of the Croods TV-Y7 Suitable for children ages 7 and older 41 2017 72 80 TV shows
603 The BFG PG for action/peril, some scary moments and brief... 60 2016 97 80 Movies and others
604 Growing Up Wild G NaN 35 2016 80 80 Movies and others

605 rows × 8 columns

In [35]:
sns.set(style="darkgrid")
sns.countplot(x="type label", data=nd, palette="Set2")
Out[35]:
<AxesSubplot:xlabel='type label', ylabel='count'>
In [36]:
labels = ['TV shows', 'Movies and others']

count = []

count.append(nd[nd['type label']=='TV shows']['type label'].count())
count.append(nd[nd['type label']=='Movies and others']['type label'].count())

lay = go.Layout(title='count')
go.Figure(data=go.Bar(x=labels,y=count),layout=lay)
Ans: From the interactive plot and data, we can say that, there are 405 TV shows and 200 Movies and other types of shows on netflix.¶
Ques: Highest rated movies/TV shows¶
In [37]:
top_5_max_values = nd['user rating score'].nlargest(5)
print(top_5_max_values)

27     99
95     99
119    99
145    99
192    99
Name: user rating score, dtype: int64
As the rating value is 99, let's see which all programs have the score of 99¶
In [38]:
nd[nd['user rating score']==99]
Out[38]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
27 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
95 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
119 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
145 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
192 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
241 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
280 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
364 13 Reasons Why TV-MA For mature audiences. May not be suitable for... 110 2017 99 80 TV shows
Ans: Above mentioned 8 shows have the highest rating. Observe that all are TV shows and of the genre TV-MA. Hence audience are liking this genre and hence the creators should focus more on it.¶

Que: In genre other than TV shows, highest rated movies?¶

In [39]:
top_5_movies = nd[nd['type label']=='Movies and others']['user rating score'].nlargest(5)
print(top_5_movies)

64     98
190    98
237    98
433    98
470    98
Name: user rating score, dtype: int64
As the rating value is 98, let's see which all programs have the score of 98¶
In [40]:
nd[(nd['type label']=='Movies and others') & (nd['user rating score']==98)]
Out[40]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
64 Finding Dory PG mild thematic elements 60 2016 98 80 Movies and others
190 Finding Dory PG mild thematic elements 60 2016 98 80 Movies and others
237 Finding Dory PG mild thematic elements 60 2016 98 80 Movies and others
433 Finding Dory PG mild thematic elements 60 2016 98 80 Movies and others
470 Finding Dory PG mild thematic elements 60 2016 98 80 Movies and others
Ans: Above mentioned 5 shows have the highest rating. Observe that all are of the genre PG. Hence audience are liking this genre and hence the creators should focus more on it.¶
In [41]:
#dict
Que: Finding the distribution of genre and the number of programs on netflix¶
In [42]:
sns.countplot(nd['rating'])

/Users/lakshmiprasanna/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Out[42]:
<AxesSubplot:xlabel='rating', ylabel='count'>
In [43]:
sns.barplot(data=nd,x='user rating score',y='rating')
Out[43]:
<AxesSubplot:xlabel='user rating score', ylabel='rating'>
In [44]:
go.Figure(data=go.Bar(x=ratings_df['Rating genre'],y=ratings_df['Count']))
Ans: We see that the count of a few types are really less, like that of NR, R, PG-13 etc, whereas of a few they are incredibly large, like TV-14 and TV-Y7-FV. What can be the reason? We can know by grilling years data.¶
Ques: YEAR ON YEAR ANALYSIS¶
In [45]:
nd.head()
Out[45]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82 80 Movies and others
1 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80 TV shows
2 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80 TV shows
3 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94 80 TV shows
4 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80 TV shows
In [46]:
year = list(nd['release year'].unique())
year.sort()
In [47]:
count_year = []
for i in year:
    count_year.append(nd[nd['release year']==i]['title'].count())
In [48]:
go.Figure(data=go.Bar(x=year,y=count_year))
Ans: We see a non uniform increase and decrease in the content released on netflix based on the year of release.¶
Ques: Year on year released based on their genre¶
In [49]:
grouped = nd.groupby('type label')

traces = []
for category, group in grouped:
    trace = go.Histogram(x=group['release year'], name=category)
    traces.append(trace)

fig = go.Figure(data=traces)

fig.show()
Ans: Based on their type, we can see that earlier we observed a domination of movies, whereas this decade has been a bloom for TV shows¶
In [50]:
grouped = nd.groupby('rating')

traces = []
for category, group in grouped:
    trace = go.Histogram(x=group['release year'], name=category)
    traces.append(trace)

fig = go.Figure(data=traces)

fig.show()
Ans: As there are a lot of genres involved, it's better to see the growth in individual graphs¶
In [51]:
for i in array_ratings:
    
    selected_values = nd['release year'].loc[nd['rating']==i]

    histogram = go.Histogram(x=selected_values)

    layout = go.Layout(title=f'Histogram for {i}')

    fig = go.Figure(data=[histogram], layout=layout)

    fig.show()
Ans: We can see the trend of each genre over the years in the above graphs. The peculiar growth or decline is found in TV-14: There has been a tremendous increase in the year 2016, from 23 to 88 shows, but then even drastic decrease from 2016, 2017, from 88 to 10. Reason can be based on user ratings¶
In [52]:
nd.head()
Out[52]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
0 White Chicks PG-13 crude and sexual humor, language and some drug... 80 2004 82 80 Movies and others
1 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80 TV shows
2 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80 TV shows
3 How I Met Your Mother TV-PG Parental guidance suggested. May not be suitab... 70 2014 94 80 TV shows
4 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80 TV shows
In [53]:
nd_tv14 = nd[nd['rating']=='TV-14'].reset_index(drop=True)
In [54]:
nd_tv14
Out[54]:
title rating ratingLevel ratingDescription release year user rating score user rating size type label
0 Grey's Anatomy TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 98 80 TV shows
1 Prison Break TV-14 Parents strongly cautioned. May be unsuitable ... 90 2008 98 80 TV shows
2 Supernatural TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 95 80 TV shows
3 The Vampire Diaries TV-14 Parents strongly cautioned. May be unsuitable ... 90 2017 91 80 TV shows
4 Pretty Little Liars TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 96 80 TV shows
... ... ... ... ... ... ... ... ...
183 Madam Secretary TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 70 80 TV shows
184 Rosewood TV-14 Parents strongly cautioned. May be unsuitable ... 90 2015 81 80 TV shows
185 Quantico TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 88 80 TV shows
186 Limitless TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 84 80 TV shows
187 Greenleaf TV-14 Parents strongly cautioned. May be unsuitable ... 90 2016 62 80 TV shows

188 rows × 8 columns

In [55]:
count_16 = nd_tv14[nd_tv14['release year']==2016]['user rating score'].count()
median_16 = nd_tv14[nd_tv14['release year']==2016]['user rating score'].median()

count_17 = nd_tv14[nd_tv14['release year']==2017]['user rating score'].count()
median_17 = nd_tv14[nd_tv14['release year']==2017]['user rating score'].median()

count_15 = nd_tv14[nd_tv14['release year']==2015]['user rating score'].count()
median_15 = nd_tv14[nd_tv14['release year']==2015]['user rating score'].median()
In [56]:
count = []
count.append([count_15,count_16,count_17])
count = [obj for sublist in count for obj in sublist]
median = []
median.append([median_15,median_16,median_17])
median = [obj for sublist in median for obj in sublist]


temp_dict = {'year': [2015,2016,2017],'count': count, 'median': median}
temp = pd.DataFrame(temp_dict)
temp = temp.set_index('year')

print(temp)

      count  median
year               
2015     23    83.0
2016     88    91.0
2017     10    91.0
Ans: Couldn't find an answer because the rating median still remained the same, it can be other production related issues or an issue outside the scope of the data.¶
Que: Ratings based on genre¶
In [57]:
for i in array_ratings:
    
    selected_values = nd['user rating score'].loc[nd['rating']==i]

    histogram = go.Histogram(x=selected_values)

    layout = go.Layout(title=f'Histogram for {i}')

    fig = go.Figure(data=[histogram], layout=layout)

    fig.show()
We can see that most shows and movies have their rating on the higher side, although there are a few genres like TV-Y7 and TV-G. Considering their weighted average is on the lower end too, the producers and story writers should invest more in the betterment of these genres and experiment.¶